• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.7-14
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• 2003

Statistics shows that 80% of freeway users travel less than 50km, and only 8% of them do over 100km. It means that the freeway is used for commuting. As a result, the freeway is not used efficiently and social cost is high. The current toll system aims for the efficient usage of the freeway by restraining short-distance trips and inducing long-distance ones. Thus, policies such as minimum toll system and discount for long-distance trips have been carried out. However. these two policies take no account of demand management between the freeway and alternative roads. They merely consider the compensation for charge on users, so the assessment of discount rate and distinction of distance are not reasonable. Consequently, not only the effective demand management but also the evaluation of those policies has been difficult to be achieved. The objective of this paper is to analyze those problematic policies and to establish a reasonable and improved toll system. In addition, this study presents the methodology to minimize social cost, which can be achieved by reducing short trips and encouraging long ones on the freeway. A new methodology of freeway toll system is applied and the results are presented.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.151-162
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• 2002

The object of this study is to develop an operating time prediction model for expressways using toll collection data. A Prediction model based on modular neural network model was developed and tested using real data. Two toll collection system(TCS) data set. Seoul-Suwon section for short range and Seoul-Daejeon section for long range, in Kyongbu expressway line were collected and analyzed. A time series analysis on TCS data indicated that operating times on both ranges are in reasonable prediction ranges. It was also found that prediction for the long section was more complex than that for the short section. However, a long term prediction for the short section turned out to be more difficult than that for the long section because of the higher sensitivity to initial condition. An application of the suggested model produced accurate prediction time. The features of suggested prediction model are in the requirement of minimum (3) input layers and in the ability of stable operating time prediction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.64-79
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• 2023

This study reviewed the process of applying the Monte Carlo simulation technique to the traffic allocation problem of metropolitan subways. The analysis applied the assumption of a normal distribution in which the travel time information of the inter-station sample is the basis of the probit model. From this, the average and standard deviation are calculated by separating the traffic between stations. A plan was proposed to apply the simulation with the weights of the in-vehicle time of individual links and the walking and dispatch interval of transfer. Long-distance traffic with a low number of samples of 50 or fewer was evaluated as a way to analyze the characteristics of similar traffic. The research results were reviewed in two directions by applying them to the Seoul Metropolitan Subway Network. The travel time between single stations on the Seolleung-Seongsu route was verified by applying random sampling to the in-vehicle time and transfer time. The assumption of a normal distribution was accepted for sample sizes of more than 50 stations according to the inter-station traffic sample of the entire Seoul Metropolitan Subway. For long-distance traffic with samples numbering less than 50, the minimum distance between stations was 122Km. Therefore, it was judged that the sample deviation equality was achieved and the inter-station mean and standard deviation of the transport card data for stations at this distance could be applied.

• Journal of the Korean Geographical Society
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• v.50 no.5
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• pp.527-541
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• 2015

The purpose of this study is to investigate the changes in the inter-city passenger flows since adapting the express rail system in Korea(KTX). For the purpose, we analyze the express bus and rail passenger annual flow data of the before and after adapting KTX. In first, we examine the changes in the time-distance parameters in the inter-city passenger flows of the express bus, general rail, and KTX by applying gravity model. The results reveal that express bus passenger flows are less sensible to the time-distance compare to the rail passenger flows, and the trends have been intensified over time. However, the time-distance parameters for the KTX passenger flows reveal the reverse relationships with the gravity model. The KTX passenger flows expand between the long distance cities. We also apply the MST(Maximum Spanning Tree) method to find out the spatial structural changes in the inter-city passenger flows. The number of hierarchies has been increased in the express bus and general rail passenger flows, while single hierarchical structure has been lasted in the KTX passenger flows.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.3
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• pp.94-104
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• 2016

In this study, factor and cluster analysis are used to classify characteristics of the administrative basic unit, "Dong", of the Incheon Metropolitan City. Travel characteristics of the classified groups are then analyzed through databases to provide directions of urban transportation planning. First, industrially developed administrative-dong show a high commuting volume by using cars. Thus the analysis indicated the need to staggering work-hour system and signal operating system policy. Second, commercially developed administrative-dong show heavy use of public transportation and long distance commute as well as high volume of shoppers. The analysis indicated the need to improve road infrastructure. Third, densely populated administrative-dong show a high rate of commute to work and school as well as long hours of commuting. Thus the analysis indicated the need to improve road transportation policies during rush hours. Fourth, administrative-dong with multiple characteristics feature heavy pedestrian traffic thus the study analyzed the need to improve pedestrian environment policies. Lastly, administrative-dong in close proximity to train stations feature extensive use of biking as well as high volume of shoppers and students commuting. Thus the study analyzed the necessity to have plans to enhance accessibility.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.157-165
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• 2006

The Purpose of this study is to analyze telecommunications impacts on travel in a comprehensive system, considering demand, supply, costs, and land use. This study first Presents a conceptual model, considering causal relationships among travel, telecommunications, land use, economic activity, and socio-demographics. Then, based on the conceptual model, the aggregate causal relationships between telecommunications (local telephone calls, toll calls, and mobile phone subscribers) and travel (vehicle-miles traveled and number of transit Passengers) are explored in a comprehensive framework, using structural equation modeling of national time series data spanning 1950-2000 in the U.S. The model results suggest that as telecommunications demand increases, travel demand increases, and vice versa. Additionally. transportation infrastructure and land use significantly affect travel demand.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.5
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• pp.1-11
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• 2009

TCS Data imply characteristics of traffic conditions. However, there are outliers in TCS data, which can not represent the travel time of the pertinent section, if these outliers are not eliminated, travel time may be distorted owing to these outliers. Various travel time can be distributed under the same section and time because the variation of the travel time is increase as the section distance is increase, which make difficult to calculate the representative of travel time. Accordingly, it is important to grasp travel time characteristics in order to compute the representative of travel time using TCS Data. In this study, after analyzing the variation ratio of the travel time according to the link distance and the level of congestion, the outlier elimination model and the smoothing model for TCS data were proposed. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variation of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.51-61
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• 2002

In this dissertation, We demonstrated the Travel Time forecasting model in the freeway of multi-section with regard of drives' attitude. Recently, the forecasted travel time that is furnished based on expected travel time data and advanced experiment isn't being able to reflect the time-lag phenomenon specially in case of long distance trip, so drivers don't believe any more forecasted travel time. And that's why the effects of ATIS(Advanced Traveler Information System) are reduced. Therefore, in this dissertation to forecast the travel time of the freeway of multi-section reflecting the time-lag phenomenon & the delay of tollgate, we used traffic volume data & TCS data that are collected by Korea Highway Cooperation. Also keep the data of mixed unusual to applicate real system. The applied model for forecasting is consisted of feed-forward structure which has three input units & two output units and the back-propagation is utilized as studying method. Furthermore, the optimal alternative was chosen through the twelve alternative ideas which is composed of the unit number of hidden-layer & repeating number which affect studying speed & forecasting capability. In order to compare the forecasting capability of developed ANN model. the algorithm which are currently used as an information source for freeway travel time. During the comparison with reference model, MSE, MARE, MAE & T-test were executed, as the result, the model which utilized the artificial neural network performed more superior forecasting capability among the comparison index. Moreover, the calculated through the particularity of data structure which was used in this experiment.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.209-221
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• 2009

Travel time estimation under given traffic conditions is important for providing drivers with travel time prediction information. But the present expressway travel time estimation process cannot calculate a reliable travel time. The objective of this study is to estimate the path travel time spent in a through lane between origin tollgates and destination tollgates on an expressway as a prerequisite result to offer reliable prediction information. Useful and abundant toll collection system (TCS) data were used. When estimating the path travel time, the path travel time is estimated combining the link travel time obtained through a preprocessing process. In the case of a lack of TCS data, the TCS travel time for previous intervals is referenced using the linear interpolation method after analyzing the increase pattern for the travel time. When the TCS data are absent over a long-term period, the dynamic travel time using the VDS time space diagram is estimated. The travel time estimated by the model proposed can be validated statistically when compared to the travel time obtained from vehicles traveling the path directly. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variaty of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• Journal of Korean Society of Transportation
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• v.23 no.6 s.84
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• pp.91-102
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• 2005

When it comes to the process of information storage, people are likely to organize individual information into the forms of groups rather than independent attributes, and put them together in their brains. Likewise, in case of finding the shortest path, this study suggests that a Hierarchical Road Network(HRN) model should be selected to browse the most desirable route, since the HRN model takes the process mentioned above into account. Moreover, most of drivers make a decision to select a route from origin to destination by road hierarchy. It says that the drivers feel difference between the link travel tine which was measured by driving and the theoretical link travel time. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Stochastic Process model uses the historical patterns of travel time conditions on links. The HRN model has compared favorably with the conventional shortest path finding model in tern of calculated speeds. Even more, the result of the shortest path using the HRN model has more similar to the survey results which was conducted to the taxi drivers. Taxi drivers have a strong knowledge of road conditions on the road networks and they are more likely to select a shortest path according to the real common sense.